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Accuracy of Laplace approximation for discrete response mixed models

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  1. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
  2. Martynas Narmontas & Petras Rupšys & Edmundas Petrauskas, 2020. "Construction of Reducible Stochastic Differential Equation Systems for Tree Height–Diameter Connections," Mathematics, MDPI, vol. 8(8), pages 1-21, August.
  3. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
  4. Giorgi, Emanuele & Diggle, Peter J., 2017. "PrevMap: An R Package for Prevalence Mapping," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i08).
  5. Noh, Maengseok & Wu, Lang & Lee, Youngjo, 2012. "Hierarchical likelihood methods for nonlinear and generalized linear mixed models with missing data and measurement errors in covariates," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 42-51.
  6. Seungwon Chung & Li Cai, 2021. "Cross-Classified Random Effects Modeling for Moderated Item Calibration," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 651-681, December.
  7. Paleti, Rajesh & Bhat, Chandra R., 2013. "The composite marginal likelihood (CML) estimation of panel ordered-response models," Journal of choice modelling, Elsevier, vol. 7(C), pages 24-43.
  8. Stringer, Alex, 2025. "Inference for generalized additive mixed models via penalized marginal likelihood," Statistics & Probability Letters, Elsevier, vol. 224(C).
  9. Wu, Jianmin & Bentler, Peter M., 2013. "Limited information estimation in binary factor analysis: A review and extension," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 392-403.
  10. Xue Wang & Jing Lu & Jiwei Zhang, 2025. "A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures," Computational Statistics, Springer, vol. 40(3), pages 1253-1284, March.
  11. Cho, S.-J. & Rabe-Hesketh, S., 2011. "Alternating imputation posterior estimation of models with crossed random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 12-25, January.
  12. Sun-Joo Cho & Sarah Brown-Schmidt & Woo-yeol Lee, 2018. "Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 751-771, September.
  13. Petras Rupšys, 2019. "Understanding the Evolution of Tree Size Diversity within the Multivariate Nonsymmetrical Diffusion Process and Information Measures," Mathematics, MDPI, vol. 7(8), pages 1-22, August.
  14. Björn Andersson & Tao Xin, 2021. "Estimation of Latent Regression Item Response Theory Models Using a Second-Order Laplace Approximation," Journal of Educational and Behavioral Statistics, , vol. 46(2), pages 244-265, April.
  15. Picchini, Umberto & Ditlevsen, Susanne, 2011. "Practical estimation of high dimensional stochastic differential mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1426-1444, March.
  16. Silvia Cagnone & Francesco Bartolucci, 2017. "Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 599-622, April.
  17. Bianconcini, Silvia & Cagnone, Silvia, 2012. "Estimation of generalized linear latent variable models via fully exponential Laplace approximation," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 183-193.
  18. Bianconcini, Silvia & Cagnone, Silvia, 2023. "The dimension-wise quadrature estimation of dynamic latent variable models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
  19. Minjeong Jeon & Sophia Rabe-Hesketh, 2012. "Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures," Journal of Educational and Behavioral Statistics, , vol. 37(4), pages 518-542, August.
  20. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
  21. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
  22. Cagnone, Silvia & Bartolucci, Francesco, 2013. "Adaptive quadrature for likelihood inference on dynamic latent variable models for time-series and panel data," MPRA Paper 51037, University Library of Munich, Germany.
  23. Yu, Dalei & Yau, Kelvin K.W., 2012. "Conditional Akaike information criterion for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 629-644.
  24. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2017. "A Variational Maximization–Maximization Algorithm for Generalized Linear Mixed Models with Crossed Random Effects," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 693-716, September.
  25. Sun-Joo Cho & Paul Boeck & Susan Embretson & Sophia Rabe-Hesketh, 2014. "Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 84-104, January.
  26. Silvia Cagnone & Paola Monari, 2013. "Latent variable models for ordinal data by using the adaptive quadrature approximation," Computational Statistics, Springer, vol. 28(2), pages 597-619, April.
  27. Peter J. Diggle & Emanuele Giorgi, 2016. "Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1096-1120, July.
  28. Jenni Niku & David I. Warton & Francis K. C. Hui & Sara Taskinen, 2017. "Generalized Linear Latent Variable Models for Multivariate Count and Biomass Data in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 498-522, December.
  29. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
  30. De la Cruz, Rolando & Meza, Cristian & Arribas-Gil, Ana & Carroll, Raymond J., 2016. "Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 94-106.
  31. L. Wu & W. Liu & X. J. Hu, 2010. "Joint Inference on HIV Viral Dynamics and Immune Suppression in Presence of Measurement Errors," Biometrics, The International Biometric Society, vol. 66(2), pages 327-335, June.
  32. Andersson, Björn & Jin, Shaobo & Zhang, Maoxin, 2023. "Fast estimation of multiple group generalized linear latent variable models for categorical observed variables," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
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